Defect inspection method and apparatus

ABSTRACT

In an ultrasonic inspection performed on an inspection object including a fine and multi-layer structure such as a semiconductor wafer and a MEMS wafer, a defect is detected by: separating a defect present inside from a normal pattern; obtaining an image of the inspection object by imaging the inspection object having a pattern formed thereon to enable a highly sensitive detection; generating a reference image that does not include a defect from the obtained image of the inspection object; generating a multi-value mask for masking a non-defective pixel from the obtained image of the inspection object; calculating a defect accuracy by matching the brightness of the image of the inspection object and the reference image; and comparing the calculated defect accuracy with the generated multi-value mask.

BACKGROUND

The present invention relates to an apparatus for inspecting a defectfrom an image of an inspection object obtained by using an ultrasonicwave, an x-ray, or the like, and specifically to an inspection methodsuitable for an inspection of an inspection body having a multi-layerstructure and a non-destructive inspection apparatus using the same.

As a non-destructive inspection method for inspecting a defect from animage of an inspection object, there are a method of using an ultrasonicimage generated by irradiating the inspection object with an ultrasonicwave and detecting a reflected wave therefrom, and a method of using anx-ray image obtained by irradiating the inspection object with an x-rayand detecting an x-ray transmitted therethrough.

In order to detect a defect present in an inspection object having amulti-layer structure using an ultrasonic wave, a reflection propertydue to difference in acoustic impedance is generally used. Theultrasonic wave propagates through a liquid or solid material andgenerates a reflected wave at an interface between materials havingdifferent acoustic impedances or at a cavity. Since a reflected wavefrom a defect is different from a reflected wave from a defect-freeportion in its strength, it is possible to obtain an image that exposesthe defect present in the inspection object by visualizing reflectionintensities at inter-layer interfaces of the inspection object.

Determination of presence of a defect in the obtained image of thereflection intensity is often performed visually by an inspector, whichmay lead to variation in the evaluation result due to the experience ofeach inspector. Moreover, major inspection objects such assemiconductors and electronic devices are increasingly miniaturized,making it more difficult to visually distinguish a defect from a normalpattern. Furthermore, multi-layer structures have become more popular tobe adapted to multi-functionalization and miniaturization of mountingproducts, a WLP (Wafer Level package) method of handling the product ina form of a wafer until the final process of packaging is becoming amainstream in the manufacturing scene. Thus, it is required for theultrasonic inspection to detect a micron-order internal defect at a highspeed with high sensitivity by separating the micron-order internaldefect from a complicated pattern in the form of the wafer. However,this corresponds to detecting only a few pixels showing the defect fromseveral tens of millions of pixels constituting an internal image, whichis nearly impossible to be determined visually.

One conventional technique of automatically detecting a defect from anultrasonic inspection image is a method described in Japanese PatentLaid-open No. 2007-101320 (Patent Document 1). This includes a functionof sequentially generating and displaying ultrasonic inspection images,thereby extracting a candidate defect based on contiguity of a luminancedistribution in each image. A defect and a noise can be distinguished bythe length of the continuous repetition of the candidate defect.Furthermore, there is another method described in Japanese PatentLaid-open No. 2012-253193 (Patent Document 2). In this method, apresence of a void in a TSV (Through Silicon Via) in a three-dimensionalintegration structure is estimated based on ultrasonic scanning.

SUMMARY

In a case where the inspection object has a complicated pattern, as wellas a multi-layer structure, of a semiconductor or an electronic device,then it is possible to distinguish a defect having a certain length anda noise generated at random times using the method described in JapanesePatent Laid-open No. 2007-101320, but impossible to distinguish betweena fine defect from a normal pattern. With the method described inJapanese Patent Laid-open No. 2012-253193, the pattern of the inspectionobject is limited to the TSV, and in order to avoid an effect by astructure that may reduce resolution of the TSV in the verticaldirection (bump electrode or wiring layer), the presence of the void inan active TSV is presumed by forming a TEG (Test Element Group) regionincluding only an etch stop layer and the TSV and inspecting thepresence of the void in the TEG region, which cannot inspect a waferwhole surface including a mixture of various patterns.

It is therefore an object of the present invention to provide aninspection method and an inspection apparatus capable of detecting aninternal fault with a high sensitivity by separating it from a normalpattern in an ultrasonic inspection performed on an inspection objectincluding a fine and multi-layer structure such as a semiconductor waferand a MEMS wafer.

To address the above problem, the present invention provides a defectinspection method of detecting a defect including the steps of:obtaining an image of an inspection object by imaging the inspectionobject having a pattern formed thereon; generating a reference imagethat does not include a defect from the obtained image of the inspectionobject; generating a multi-value mask for masking a non-defective pixelfrom the obtained image of the inspection object; calculating a defectaccuracy by matching the brightness of the image of the inspectionobject and the reference image; and comparing the calculated defectaccuracy with the generated multi-value mask.

To address the above problem, the present invention also provides adefect inspection apparatus including: an image acquisition unitobtaining an image of an inspection object by imaging the inspectionobject having a pattern thereon; a reference image generation unitgenerating a reference image that does not include a defect from theimage of the inspection object obtained by the image acquisition unitand generating a multi-value mask for masking a non-defective pixel fromthe obtained images of the inspection object; a feature amount computingunit calculating a defect accuracy by matching the brightness of theimage of the inspection object obtained by the image acquisition unitand the reference image generated by the reference image generationunit; and a defect detection processing unit detecting the defect bycomparing the defect accuracy calculated by the feature amount computingunit with the multi-value mask generated by the reference imagegeneration unit.

Moreover, to address the above problem, the present invention furtherprovides an ultrasonic inspection apparatus including: a detection unitincluding an ultrasonic probe emitting an ultrasonic wave and a flawdetector detecting a reflected echo generated from an inspection objectby the ultrasonic wave emitted from the ultrasonic probe; an A/Dconversion unit A/D converting a signal output from the flaw detectorhaving detected the reflected echo in the detection unit; and an imageprocessing unit detecting the reflected echo from the flaw detectorconverted into a digital signal by the A/D conversion unit, processingthe output signal, generating a sectional image in a plane parallel witha surface of the inspection object inside the inspection object,processing the generated internal sectional image, and therebyinspecting an internal defect of the inspection object, wherein theimage processing unit includes: a sectional image generation unitdetecting the reflected echo generated from the flaw detector,processing the output signal, and generating the sectional image of theinside of the inspection object; a reference image generation unitgenerating a reference image that does not include a defect from thesectional image of the inside of the inspection object generated by thesectional image generation unit and generating a multi-value mask formasking a non-defective pixel from the obtained internal image of theinspection object; a feature amount computing unit calculating a defectaccuracy by matching the brightness of the image of the inspectionobject obtained by the image acquisition unit and the reference imagegenerated by the reference image generation unit; a defect detectionprocessing unit detecting the defect by comparing the defect accuracycalculated by the feature amount computing unit with the multi-valuemask generated by the reference image generation unit; and an outputunit outputting the internal defect detected by the defect detectionprocessing unit.

The present invention makes it possible to detect and output a finedefect near a normal pattern on an internal image of the inspectionobject including a mixture of aperiodic and complicated patterns.

Moreover, the present invention also makes it possible to detect thedefect inside the inspection object by processing the sectional image ofthe inside of the inspection object detected using an ultrasonic wave.

These features and advantages of the invention will be apparent from thefollowing more particular description of preferred embodiments of theinvention, as illustrated in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary flow chart of a process showing a concept of amethod for inspecting an internal defect of a wafer carrying variousdevices thereon according to a first embodiment of the presentinvention;

FIG. 2 is a block diagram showing a concept of an ultrasonic inspectionapparatus according to the first embodiment of the present invention;

FIG. 3 is a block diagram showing a configuration of the ultrasonicinspection apparatus according to the first embodiment of the presentinvention;

FIG. 4 is a perspective view of a wafer having a multi-layer structureused as an inspection object in the first embodiment of the presentinvention;

FIG. 5A is a sectional view of the multi-layer wafer showing a relationbetween the multi-layer wafer and an ultrasonic probe used as theinspection object in the first embodiment of the present invention;

FIG. 5B is a graph showing a reflected echo signal from the multi-layerwafer detected by using the ultrasonic probe used as the inspectionobject in the first embodiment of the present invention;

FIG. 6A is a plan view of the multi-layer wafer used as the inspectionobject in the first embodiment of the present invention;

FIG. 6B is an image of the multi-layer wafer used as the inspectionobject in the first embodiment of the present invention;

FIG. 7 is a plan view of the wafer with a label applied to each chip ofthe multi-layer wafer used as the inspection object in the firstembodiment of the present invention;

FIG. 8 is a block diagram showing a configuration of a defect detectionunit of the ultrasonic inspection apparatus according to the firstembodiment of the present invention;

FIG. 9A is a block diagram showing a configuration of a reference imagegeneration unit of the ultrasonic inspection apparatus according to thefirst embodiment of the present invention;

FIG. 9B is a process flow chart of the reference image generation unitin the defect detection unit of the ultrasonic inspection apparatusaccording to the first embodiment of the present invention;

FIG. 10 shows an image and a graph showing a procedure of generating amulti-value mask by the defect detection unit of the ultrasonicinspection apparatus according to the first embodiment of the presentinvention;

FIG. 11 is a flow chart showing a defect detection process by the defectdetection unit of the ultrasonic inspection apparatus according to thefirst embodiment of the present invention;

FIG. 12A is a plan view of the wafer labeled with respect to eachpattern group according to the first embodiment of the presentinvention;

FIG. 12B is a plan view of chips on the wafer showing an example inwhich information of the defect detected with respect to each group isintegrated and output by a defect information output unit in the defectdetection unit of the ultrasonic inspection apparatus according to thefirst embodiment of the present invention;

FIG. 12C is a plan view of the wafer showing another example in whichinformation of the defect detected with respect to each group isintegrated and output by a defect information output unit in the defectdetection unit of the ultrasonic inspection apparatus according to thefirst embodiment of the present invention;

FIG. 13A is a plan view of the wafer labeled with respect to eachpattern group according to the first embodiment of the presentinvention;

FIG. 13B is a flow chart showing a process by the defect detection unitof the ultrasonic inspection apparatus according to the first embodimentof the present invention but different from what is described withreference to FIG. 12B;

FIG. 14 shows a perspective view of the wafer and an image of chipsshowing an example of grouping on the multi-layer wafer used as theinspection object according to the first embodiment of the presentinvention;

FIG. 15A is a plan view of an IC tray used as an inspection objectaccording to a second embodiment of the present invention; and

FIG. 15B is a flow chart showing a process for the IC tray used as theinspection object according to the second embodiment of the presentinvention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention relates to a defect inspection method making itpossible to separate signals of a normal pattern from that of a defecton an inspection object including an aperiodic pattern structure andthereby detecting a fine defect, and an apparatus for the same. That is,the present invention is configured to segment an image into regionseach consisting of the same pattern group, group the regions, and detecta defect in a partial image of the same group, even if the imageobtained from the inspection object includes an aperiodic pattern. Thepresent invention is effective for an appearance inspection, anon-destructive inspection, and the like performed on such an inspectionobject having a complicated pattern structure.

Moreover, the present invention is configured to detect a defect in aninternal image of the inspection object by segmenting an image intoregions each consisting of the same pattern group, grouping the regions,and integrating features of the segmented internal images belonging tothe group. Grouping is performed based on labels applied to segmentedregions by a user in advance, or based on design data or an exposurerecipe used when patterning each layer. Moreover, for detection of thedefect, a reference segmented internal image is formed by integratingthe features of the segmented internal images belonging to the samegroup, and the features are compared between the reference segmentedinternal image and each segmented internal image to calculate a defectaccuracy. Furthermore, with respect to each pixel having a defectaccuracy, a multi-value mask is generated from the segmented internalimage, masking is performed on the pixel having the defect accuracyusing the multi-value mask, and the remaining pixels are determined tobe defective. By performing this on each group, the non-destructiveinspection can be performed on the entire region of the inspectionobject covering a wide range.

Hereinbelow, embodiments of the present invention will be described withreference to drawings.

First Embodiment

Hereinbelow, an explanation of a case where a defect inspection methodaccording to the present invention is applied to an ultrasonicinspection apparatus.

An implementation of the inspection method according to the presentinvention and the apparatus thereof is described with reference to FIGS.1 to 14. First, an implementation of the ultrasonic inspection apparatususing a substrate having a multi-layer structure and a complicatedpattern, such as a semiconductor wafer and a MEMS (Micro ElectroMechanical System) wafer, as the inspection object is described.

As a property of an ultrasonic wave, it propagates through theinspection object and, if there is a boundary at which a materialproperty (acoustic impedance) changes, it is partially reflected. Sincea large part of the ultrasonic wave is reflected when there is a cavity,such a defect as a void or a stripping can be detected with a highsensitivity based on the reflection intensity especially at a bondingsurface of the wafer including multiple layers bonded together.Hereinbelow, a defect on the bonding surface of the multi-layer wafer isto be detected.

FIG. 2 is a conceptual diagram showing the implementation of theultrasonic inspection apparatus according to the present invention. Theultrasonic inspection apparatus according to the present inventionincludes a detection unit 1, an A/D convertor 6, an image processingunit 7, and a total control unit 8.

The detection unit 1 includes an ultrasonic probe 2 and a flaw detector3. The flaw detector 3 drives the ultrasonic probe 2 by applying a pulsesignal to the ultrasonic probe 2. The ultrasonic probe 2 driven by theflaw detector 3 generates an ultrasonic wave and emits it toward theinspection object (sample 5). When the emitted ultrasonic wave entersthe sample 5 having the multi-layer structure, a reflected echo 4 isgenerated from the surface of the sample 5 or from the bonding surfaceof the wafer. The reflected echo 4 is then received by the ultrasonicprobe 2, processed by the flaw detector 3 as needed, and converted intoa reflection intensity signal.

The reflection intensity signal is then converted into digital waveformdata by the A/D convertor 6 and input to the image processing unit 7.The image processing unit 7 appropriately includes an image generationunit 7-1, a defect detection unit 7-2, and a data output unit 7-3.Signal conversion to be described later is performed by the imagegeneration unit 7-1 on the waveform data input from the A/D convertor 6to the image processing unit 7, thereby generating a sectional image ofa specific bonding surface of the sample 5 from the digital waveformdata. The defect detection unit 7-2 performs a process to be describedlater based on the sectional image of the bonding surface generated bythe image generation unit 7-1 to detect the defect. The data output unit7-3 generates data to be output as an inspection result such asinformation about an individual defect detected by the defect detectionunit 7-2 and an image for observation of the section, and outputs thedata to the total control unit 8.

Shown in FIG. 3 is a schematic diagram of an exemplary configuration ofa specific ultrasonic inspection apparatus 100 implementing theconfiguration shown in FIG. 2. In FIG. 3, denoted by 10 is a coordinatesystem having three orthogonal axes of X, Y, and Z.

Reference numeral 1 in FIG. 3 corresponds to the detection unit 1described with reference to FIG. 2. Denoted by 11 included in thedetection unit 1 is a scanner table, 12 is a tank arranged on thescanner table 11, and 13 is a scanner arranged so as to bridge over thetank 12 on the scanner table 11 and movable in X, Y, and Z directions.The scanner table 11 is a base placed substantially horizontal. The tank12 contains water 14 injected to the height indicated by a dotted line,and the sample 5 is placed on the bottom (in the water) of the tank 12.The sample 5 is the semiconductor wafer including the multi-layerstructure and the like, as described above. The water 14 is a mediumrequired for effectively propagating the ultrasonic wave emitted by theultrasonic probe 2 into the sample 5. Denoted by 16 is a mechanicalcontroller, which drives the scanner 13 in the X, Y, and Z directions.

For the sample 5, the ultrasonic probe 2 emits the ultrasonic wave froman ultrasonic output unit at its lower edge, and receives a reflectedecho returned from the sample 5. The ultrasonic probe 2 is attached to aholder 15 and movable in the X, Y, and Z directions by the scanner 13driven by the mechanical controller 16. Thus, the ultrasonic probe 2 canreceive the reflected echo at a plurality of measurement points of thesample 5 set in advance while travelling in the X and Y directions,obtain a two-dimensional image of a bonding surface within a measurementrange (X-Y plane), and thus inspect the defect. The ultrasonic probe 2is connected to the flaw detector 3 that converts the reflected echointo a reflection intensity signal via a cable 22.

The ultrasonic inspection apparatus 100 further includes the A/Dconvertor 6 that converts the reflection intensity signal output fromthe flaw detector 3 of the detection unit 1 into a digital waveform, theimage processing unit 7 that processes an image signal having been A/Dconverted by the A/D convertor 6, the total control unit 8 that controlsthe detection unit 1, the A/D convertor 6, and the image processing unit7, and the mechanical controller 16.

The image processing unit 7 processes the image signal having been A/Dconverted by the A/D convertor 6 and detects an internal defect of thesample 5. The image processing unit 7 includes the image generation unit7-1, the defect detection unit 7-2, the data output unit 7-3, and aparameter setting unit 7-4.

The image generation unit 7-1 generates an image from the digital dataobtained by A/D converting the reflected echo returned from the samplesurface and each bonding surface, and the like within the measurementrange of the sample 5 set in advance and the position information of theultrasonic probe obtained by the mechanical controller 16. The defectdetection unit 7-2 processes the image generated by the image generationunit 7-1 and thereby becomes apparent or detects the internal defect.The data output unit 7-3 output the inspection result from becomingapparent or detecting the internal defect by the defect detection unit7-2. The parameter setting unit 7-4 receives a parameter such as ameasurement condition input from the outside, and sets the parameter tothe defect detection unit 7-2 and the data output unit 7-3. In the imageprocessing unit 7, for example, the parameter setting unit 7-4 isconnected to a storage unit 18 that stores therein a database.

The total control unit 8 includes a CPU (incorporated in the totalcontrol unit 8) that performs various controls, receives the parameteror the like from the user, and appropriately connects a user interfaceunit (GUI unit) 17 that includes a display means for displayinginformation including an image of the defect detected by the imageprocessing unit 7, the number of defects, a coordinate and dimension ofthe individual defect, and the like, and an input means, and the storageunit 18 storing therein the feature amount, image, and the like of thedefect detected by the image processing unit 7. The mechanicalcontroller 16 drives the scanner 13 based on a control instruction fromthe total control unit 8. It should be noted that the image processingunit 7, the flaw detector 3, and the like are also driven by theinstruction from the total control unit 8.

FIG. 4 shows a configuration of an inspection object 400 as an exampleof the sample 5. The inspection object 400 shown in FIG. 4 schematicallyrepresents appearance of a wafer including the multi-layer structurewhich is the main inspection object. The inspection object 400 is alaminated wafer formed by laminating and bonding wafers 41 to 45 ofdifferent types such as MEMS, CPU, memory, CMOS, and the like. Thenumber of lamination is not limited to five but may be any number largerthan one. The ultrasonic inspection apparatus 100 according to thepresent invention is used to inspect whether the wafers 41 to 45 in theinspection object 400 are properly bonded together on the wholelamination surface (bonding surface) without forming any depleted regionsuch as a void or a stripping.

FIG. 5A is an example schematically showing a vertical structure of theinspection object 400 having the multi-layer structure shown in FIG. 4.When an ultrasonic wave 50 emitted from the ultrasonic probe 2 enters asurface 401 of the inspection object 400, the ultrasonic wave 50transfers through the inspection object 400 and is reflected from theinspection object surface 401 and bonding surfaces 402, 403, 404, 405between the wafers due to difference in acoustic impedance, and theultrasonic probe 2 receives them as a single reflected echo.

A graph 51 in FIG. 5B shows an exemplary reflected echo from theinspection object received by the ultrasonic probe 2, with its abscissarepresenting time and ordinate representing reflection intensity. Timealso indicates the depth of the inspection object 400. In the graph 51,by applying a visualization gate 52 (hereinbelow, simply referred to as“gate 52”) to a time domain that may include the reflected echo from thebonding surface to be observed, the desired time domain is cut out and apeak value in the gate 52 is detected.

The image generation unit 7-1 of the image processing unit 7 detects thepeak value in each scanning position from the reflected echo obtainedwhile scanning the measurement range (X-Y plane) by the scanner 13 andconverts the peak value into a gray value (for example, 0 to 255 in acase of generating a 256-tone image), thereby generating the sectionalimage of the bonding surface (an image of a section (a plane parallel tothe wafer surface) in a depth direction from the wafer surface) from thegray value information at each scanning position.

Now, when the inspection object has the multi-layer structure like theinspection object 400 and has a plurality of bonding surfaces (such as402 to 405) to be inspected, it is possible to set the gate 52 to thereflected echo in the time domain corresponding to each bonding surfaceand generate the sectional image of each bonding surface.

Shown in FIGS. 6A and 6B are exemplary sectional images of the bondingsurface generated. FIG. 6A schematically shows a top view of a laminatedwafer 60 that is the inspection object. The laminated wafer 60 iseventually diced along straight lines shown in FIG. 6A to become afinished product. Hereinbelow, “chip” is used to refer to the dicedproduct. Denoted by 62 in (a) of FIG. 6B is an exemplary sectional imageof the bonding surface obtained from a region 61 delimited by a brokenline and including three chips on the laminated wafer 60. Denoted by 63,64, and 65 in (b) of FIG. 6B are partial sectional images made bysegmenting the sectional image 62 in (a) of FIG. 6B into three regionscorresponding to each chip. Since the partial sectional images 63 and 65in (b) of FIG. 6B has the same devices mounted on the chip, the patternconfigurations included in the obtained partial sectional image(hereinbelow, referred to as “pattern group”) are also the same, whilethe left half of the partial sectional image 64 in (b) of FIG. 6B isconstituted by two patterns, indicating that its pattern group isdifferent from that of the partial sectional images 63 and 65.

According to this embodiment, for such an inspection object constitutedby multiple types of chips having different pattern groups, thesectional images are grouped with respect to each region having the samepattern group (for example, the partial sectional images 63 and 65belong to a group A and the partial sectional image 64 belong to a groupB), and the defect detection process is performed with respect to eachgroup.

FIG. 1 is the conceptual diagram of this case. Denoted by 101 is anappearance of a wafer including a mixture of various devices thereon asan example of the inspection object. The inspection object (wafer) 101includes chips formed thereon in a grid shape, and the different hatchpatterns indicate different types of the devices constituting the chip.In other words, basically the inspection images constituted by the samepattern group are obtained from the regions of the same hatch pattern.

In the defection inspection according to the invention, the detectionunit 1 obtains a surface image or an internal sectional image of theinspection object 101 (S11), and the image processing unit 7 firstextracts partial images constituted by the same pattern group from theobtained surface image or the internal sectional image of the inspectionobject 101 (S12). The partial images corresponding to the regions 103,104 of the wave hatch pattern in the inspection object 101 are extractedfrom the extracted partial image and aligned as shown by 102 (S13). Theimage alignment means, because the extracted partial image 103 to 108have the same pattern group, performing a position correction so thatregions of the same pattern may be present at the same coordinate valuein each image.

Features are then calculated in each pixel of each image and integratedbetween images as denoted by 109 and 110 (S14). This step is performedon all the pixels in the partial images to generate a reference partialimage 111 (S15) and generate a multi-value mask 112 (S16). Integralcomparison (S17) with the generated reference partial image 111 and themulti-value mask 112 is then performed on each of the partial image 103to 108 to detect a defect 113. Finally, the detected defect 113 iscombined on the wafer level (S18) and the result is displayed (S19). Thesame process is performed on the partial images constituted by otherpattern groups (images corresponding to the striped, dotted, orcheckerboard hatch patterns on the wafer 101).

Here, extracting the partial images having the same pattern group fromthe inspection object (wafer) 101 used as the inspection object at StepS12 is performed by receiving a prior setting from the user. FIG. 7shows an example thereof. Denoted by 60 in (a) of FIG. 7 is a layout ofchips formed on the wafer 101 used as the inspection object. This isdisplayed on a screen by the user interface unit 17 shown in FIG. 3, andthe parameter setting unit 7-4 receives the labels applied to theindividual chip on the screen by the user. In this process, theinspection object 101 is grouped based on the labels applied by theuser.

Denoted by 701 in (b) of FIG. 7 is an example of the result, which isformed by segmenting the wafer 101 used as the inspection object intopartial images in the unit of chips and grouping the partial images intofour categories of A to D based on the labels applied by the user. Anautomatic setting is also possible using the recipe for the exposureeven if there is no user setting. The exposure recipe includes exposureposition information indicative of where to print a circuit pattern onthe substrate, exposure order, and the like, from which the informationabout the pattern to be formed at each position can be obtained.

Next, a configuration of the process performed by the defect detectionunit 7-2 of the image processing unit 7 is described. FIG. 8 shows anexample thereof. The defect detection process is performed using thepartial images constituted by the same pattern group. An inspectionrecipe 801 constituted by various parameter values used for theprocessing, and an image of the wafer whole surface 802 are input. Thedefect detection unit 7-2 generally includes a partial image groupgeneration unit 81, a reference image generation unit 82, a defectdetection processing unit 83, and a defect information output unit 84.First, when the wafer whole surface 802 is input to the defect detectionunit 7-2, a plurality of partial images applied with the same label bythe partial image group generation unit 81 (for example, 103 to 108 inFIG. 1) are input to the reference image generation unit 82. Thereference image generation unit 82 generates a reference partial image804 and a multi-value mask 805. The reference partial image 804 meansthe normal image constituted by the same pattern group as that of theinput partial image.

Shown in FIGS. 9A and 9B is an example of a method of generating thereference partial image. Denoted by 90 a, 91 a, 92 a, . . . in FIG. 9Bare the partial images of the same label cut out of the inspectionobject 101. These partial images include the same pattern group (denotedherein by three different hatch patterns 911 to 913). The defects 921 to923 (indicated by white color) may possibly be included. There also maybe a positional shift of the pattern due to a slight difference in theposition of obtaining the image when scanning (sampling error)(indicated by difference in positions of the hatch patterns 911 to 913with respect to the black background). Thus, correction of the positionof each image, namely inter-image position correction is performed so asto correct the position of the partial image, or so as to align thecoordinates of the hatch patterns 911 to 913 with the black background(S901).

The position correction between the partial images at Step S901 isperformed using a general matching method such as: specifying onepartial image; calculating a shift amount that makes the minimum sum ofsquares of the luminance difference between the specified image andother partial images to be corrected while shifting the partial image tobe corrected with respect to the specified image, or calculating a shiftamount that makes the maximum normalized cross-correlation coefficient;and shifting the partial image by the calculated shift amount. Denotedby 90 b, 91 b, 92 b, . . . in FIG. 9B are the partial images after theposition correction.

The features of the pixels in the partial images 90 b, 91 b, 92 b, . . .after the position correction are then calculated (S902). The featuremay be any of a contrast in each pixel (Equation 1) (luminance gradientwith peripheral pixels), a luminance average including proximate pixels(Equation 2), a luminance dispersion value (Equation 3), increase ordecrease of the brightness and its maximum gradient direction withrespect to the proximate pixels, which represents the feature of thepixel.

$\begin{matrix}\lbrack {{Equation}\mspace{14mu} 1} \rbrack & \; \\{{{F\; 1( {x,y} )};}{{\max\{ {{f( {x,y} )}{f( {{x + 1},y} )}{f( {x,{y + 1}} )}{f( {{x + 1},{y + 1}} )}} \}} - {{mi}\underset{\_}{n}\{ {{f( {x,y} )}{f( {{x + 1},y} )}{f( {x,{y + 1}} )}{f( {{x + 1},{y + 1}} )}} }}} & ( {{Equation}\mspace{14mu} 1} ) \\\lbrack {{Equation}\mspace{14mu} 2} \rbrack & \; \\{{{F\; 2( {x,y} )};}\Sigma\;{{f( {{x + i},{y + j}} )}/{M( {i,{j = {{{- 1}01\mspace{14mu} M} = 9}}} )}}} & ( {{Equation}\mspace{14mu} 2} ) \\\lbrack {{Equation}\mspace{14mu} 3} \rbrack & \; \\{{{{F\; 3( {x,y} )};}\lbrack {{\Sigma\{ {f\;( {{x + i},{y + j}} )^{2}} \}} - {\{ {\Sigma\;{f( {{x + i},{y + j}} )}} \}^{2}/M}} \rbrack}/( {M - 1} )} & ( {{Equation}\mspace{14mu} 3} ) \\{i,{j = {{{- 1}01\mspace{14mu} M} = 9}}} & \;\end{matrix}$

where f(x, y) is the luminance value of the coordinate (x, y) in thepartial image.

Next, as described above, the feature of each pixel (x, y) calculatedfor each partial image is integrated between the partial images (S903)to generate the reference partial image 804. One example of thisprocessing method includes: collecting features Fi(x, y) of thecorresponding coordinate (x, y) between partial images (i is the numberdesignated to the partial image), and thereby statistically determiningthe reference feature value S(x, y) of the feature of each pixel asrepresented by Equation 4. The luminance value of the partial imageequal to the reference feature value is determined as the luminancevalue of the reference partial image. In this manner, the referencepartial image 804 exclusive of influences from a defect is generated.[Equation 4]S(x,y)=Median{F1(x,y),F2(x,y),F3(x,y), . . . }   (Equation 4)

Median: Function outputting a median value (median) of the feature ofeach partial image

S(x, y): Reference feature value

F_(*)(x, y): Feature value of the partial images 90 b, 91 b, 92 b, . . .after position correction

It is noted that, as represented by Equation 5, the statisticalprocessing may be performed by calculating an average of the feature atthe corresponding coordinate between images and using the luminancevalue of the partial image having its feature closest to the average asthe luminance value of the reference partial image.[Equation 5]S(x,y)=Σ{F _(i)(x,y)}/N  (Equation 5)i: the number designated to the partial imageN: partial image

As shown in FIG. 8, the reference image generation unit 82 generates, inaddition to the reference partial image, the multi-value mask 805 foreliminating (masking) a non-defective pixel between images. One exampleof the generation procedure is shown in FIG. 10. The multi-value maskaccording to this embodiment is set by calculating multiple values (0 to255) with respect to each pixel in the image. For the partial images 90b, 91 b, 92 b, . . . after the position correction shown in FIG. 9B, theluminance value f(x, y) of the corresponding pixel is integrated, andthe dispersion value of the luminance values is calculated as thefeature according to Equation 6.

In FIG. 10, a graph 1001 shows a distribution of luminance values of acoordinate indicated by a white square 1011 in the partial images 90 b,91 b, 92 b, . . . , showing that the dispersion value σ1 is calculatedby integrating the luminance values between the images. A graph 1002shows the distribution of the luminance values of the coordinateindicated by a black square 1012 in the partial images 90 b, 91 b, 92 b,. . . , showing that the dispersion value σ2 is calculated byintegrating the luminance values between the images. The dispersionvalue σ is calculated for all the pixels within the partial images inthe same manner.

Another feature is also calculated from the same pixel. Referencenumeral 1003 shows a pattern near the coordinate indicated by the blacksquare 1012. There is a longitudinal pattern 1004 with high luminance. Acurve 1021 in the graph 1020 shows a luminance profile of a locationindicated by an arrow 1005 (→ ←) on the longitudinal pattern 1004 in thepattern 1003. A curve 1022 shows a luminance profile when thelongitudinal pattern 1004 in the pattern 1003 is shifted by an amount a.Thus, Δ in the graph 1020 indicates the luminance difference caused bythe positional shift by the amount a. The Δ is regarded as the secondfeature of the pixel indicated by the black square 1012. The luminancedifference Δ is calculated for all the pixels within the partial imagesin the same manner. Then, based on the values of the two features σ andΔ calculated from all the pixels within the partial images, amulti-value mask value M is calculated according to Equation 7. Anaspect 1031 in the three-dimensional graph 1030 corresponds to the valueM of the multi-value mask calculated from Δ and σ.[Equation 6]σ(x,y)=┌Σ{f _(i)(x,y)² }−{Σf _(i)(x,y)}² /N┐/(N−1)  (Equation 6)i: the number designated to the partial imageN: partial image[Equation 7]M(x,y)=k×σ(x,y)+m×Δ(x,y)+n   (Equation 7)

Since σ and Δ are calculated from the features of each pixel, the valueM of the multi-value mask is calculated separately with respect to eachpixel according to σ and Δ. This may cause a difference in the patternluminance values between partial images, despite the same pattern group,due to a fabrication tolerance or a sampling error at the time of imageacquisition, and the difference is reflected on the mask.

The parameters α (described in FIG. 10), k, m, and n are set in advance,and the distribution of the multi-value mask M indicated by the aspect1031 in the three-dimensional graph 1030 can be adjusted by adjustingthese parameters. In addition, although the example was given in whichthe multi-value mask M was calculated based on σ and Δ calculated byintegrating the features of each pixel between the partial images, anyfeature indicative of the property of the pixel can be used, and the wayof integrating the feature may also be changed accordingly. Furthermore,the number of the features to be integrated is not limited to two butthe multi-value mask M can be calculated from any number more than oneof the integration features. Although the value of n was described as afixed value, it can be set with respect to each pixel in the partialimage.

Although the above description was given taking an example of generatingthe reference partial image exclusive of any defect from partial images,the reference image may also be generated by cutting out partial imagesconstituted by the same pattern group from a good sample guaranteed tobe free of defect.

Hereinbelow, the defect detection processing unit 83 that detects adefect from the partial images 103 to 108 is described using thereference partial image 804 and the multi-value mask 805 in FIG. 8.

FIG. 11 shows an example of the process performed by the defectdetection processing unit 83. The reference partial image 804 and themulti-value mask 805 output from the reference image generation unit 82and a partial image group 803 of the inspection object (partial imagesin the same group) are input, which images have been subjected to aninter-image position correction, as described with reference to FIG. 9.

First, each image in the partial image group used as the inspectionobject is matched with the reference partial image for the brightness,as needed (S1101). There may be difference in brightness even betweenthe partial images constituted by the same pattern group, due todifference in thickness of each layer when the sample is formed of amulti-layer film, or due to warpage of a wafer when the inspectionobject is the wafer. Therefore, matching of the brightness is performed(correct the brightness of one image so that they have the samebrightness).

One example of the method thereof described herein includes the step ofcorrecting the brightness of the partial image to match that of thereference partial image 804 based on the least squares approximation.Assuming that there is a linear relation represented by Equation 8between the pixels f(x, y) and g(x, y) of the respective image in thepartial image group 803 and the reference partial image 804, a and b arecalculated so that Equation 9 makes the minimum value and they are usedas correction coefficients “gain” and “offset”. The brightness iscorrected on the corresponding pixels in respective images in thepartial image group 803 and the reference partial image 804 as well asall the pixel values f(x, y) in the partial images to be corrected forthe brightness, as represented by Equation 10[Equation 8]g(x,y)=a+b·f(x,y)   (Equation 8)[Equation 9]Σ{g(x,y)−(a+b·f(x,y))}²   (Equation 9)[Equation 10]f′(x,y))=gain·f(x,y)+offset   (Equation 10)

The defect accuracy is then calculated for each pixel in a partial image1110 (S1102). An exemplary defect accuracy is defined by a valueindicative of an appearance at the normal time, namely a degree ofdeviation from the luminance value of the reference partial image 804,which is calculated according to Equation 11.[Equation 11]d(x,y)=f′(x,y)−g(x,y)   (Equation 11)

The masking process is performed on the defect accuracy calculatedaccording to Equation 11 using the multi-value mask 805 for each pixel,and the remaining pixels are detected as defective (S1103).

The masking process detects the defect when the defect accuracy exceedsa mask value as represented by Equation 12.[Equation 12]P(x,y):defect (if d(x,y)≥M((x,y)P(x,y:normal (if d(x,y9<M(x,y)where, m(x,y)=k×σ(x,y)+m×Δ(x,y)+n(x,y)   (Equation 12)

It is noted that, although the example of detecting the defect bymasking the pixels brighter than the luminance value of the referencepartial image 804 is described above, the same applied to the pixelsdarker than the luminance value of the reference partial image 804. Asalready described, an influence by the fabrication tolerance and thesampling error at the time of image acquisition between the images istaken into account for the multi-value mask 805. Thus, the multi-valuemask 805 can mask the pixels including a noise of the fabricationtolerance or the sampling error in the defect accuracy calculatedaccording to Equation 11.

Finally, the defect feature of a defective pixel is calculated fordetermining whether it is defective or not (S1104). There may be one ormore defect features indicative of the feature of the defect. Examplesinclude an area, a maximum length, a luminance value, an edge intensity,and the like of the defect.

The process steps S1101 to S1104 by the defect detection processing unit83 described above are performed on the partial images constituted bythe same pattern group after grouping, and the same is performed on eachgroup.

As described above, the information about the defect detected by theprocess per group is then rearranged into a chip array on the inspectionobject by the defect information output unit 84. Its concept is shown inFIGS. 12A and 12B.

A wafer 120 shown in FIG. 12A is an inspection object segmented intoregions constituted by the same pattern group and labeled. Based onthis, it is assumed that the defect detection process is performed oneach of the groups A to D to detect a defect 1202 a in a region 1202 ofthe group A, a defect 1201 a in a region 1201 of the group B, a defect1203 a in a region 1203 of a group C, and a defect 1204 a in a region1204 of a group D, as shown in FIG. 12B.

Upon receipt of this result, the defect information output unit 84 inFIG. 8 rearranges the output result from the segmented partial imagesbased on region arrangement information of the inspection object (wafer)120. That is, it maps the detected results 1201 a to 1204 a at thepositions of regions 1201 to 1204 in FIG. 8B, generates a defectdistribution image 121 on the wafer, and output the defect distributionimage 121. The defects at 1202 a and 1203 a detected in separateprocesses are thus output as a single defect. At the same time, thecoordinate indicative of the defect position in the partial image isconverted into the coordinate system of the inspection object 101, andseparately calculated defect features (area, maximum length, etc.) arealso integrated. The defect information after the conversion andintegration is output to the data output unit 7-3 and displayed by adisplay means such as a display unit via the user interface unit (GUIunit) 17. It is also possible to simultaneously determining whether thechip is good or defective based on the defect features and display theresult. For example, the number of defects, the maximum defective area,the ratio of the defective pixels in the chip are measured, and the chipexceeding a judgement condition input as the inspection recipe is outputand displayed as a faulty chip.

Although FIG. 12B shows an example of mapping the detected result andoutputting the defect distribution image on the wafer as denoted by 121,it is also possible to display the defective chips in a color differentfrom that of defect-free chips on the wafer, as shown in FIG. 12C.

For inspecting the wafer, the defect detection process also has aplurality of detection methods other than using the luminance differencecompared with the reference image as the defect accuracy, as describedabove. Its concept is shown in FIGS. 13A and 13B. FIG. 13A shows anwafer 130 used as the inspection object segmented into regionsconstituted by the same pattern group and labeled. In this example, thegroup A includes seven regions, the group B includes nine regions, thegroup C includes three regions, and the group D includes two regions.

The defect detection process according to this embodiment can change themethod of detecting the defect depending on the number of the regionshaving the same label. For example, as described above, the referencepartial image with the influence by the defect removed is statisticallygenerated by integrating the features of each partial image. However, asthe number of the partial images decrease, reliability of thestatistical processing decreases. Therefore when the number of theregions is smaller than a certain number (for example, less than fourregions), the statistical processing is not performed, but comparisonbetween actual subjects, comparison with a model, comparison with afixed threshold, and the like may be performed. An exemplary processingin the case of three partial images like the group C is as follows.

Denoted by 131, 132, 133 in FIG. 13B are partial images generated bycutting out the regions corresponding to the label C in the inspectionobject (wafer) 130 and performing position correction and brightnessmatching, where the partial images (hereinbelow, referred to simply asimages) 132 and 133 include defects 1321 and 1331, respectively. Forthese three images, differences among them (difference image: absolutevalue here) are computed. A difference image 131 a takes a differencebetween the images 131 and 132, a difference image 132 a takes adifference between the images 132 and 133, and a difference image 133 atakes a difference between the images 133 and 131. The defective portionbecomes apparent. Furthermore, the defect accuracy is calculated bytaking the minimum value from the differences between two images. Thatis, a difference image 131 b is the minimum value between the differenceimage 133 a and the difference image 131 a, a difference image 132 b isthe minimum value between the difference image 131 a and the differenceimage 132 a, and a difference image 133 b is the minimum value betweenthe difference image 132 a and the difference image 133 a; thedifference image 131 b is the defect accuracy of the image 131, thedifference image 132 b is the defect accuracy of the image 132, and thedifference image 133 b is the defect accuracy of the image 133. Thedefects 1321 and 1331 are detected by masking them with the fixed valueor the multi-value mask.

As another example of processing, when there are two or less partialimages like the group D, it is also possible to detect the defect byperforming a processing similar to that shown in FIG. 11 using thereference partial image extracted from a good sample as an input. It isalso possible as another example to detect the defect regarding theluminance value itself in an unmasked area as the defect accuracy usinga binary mask that can fully mask a non-inspection area (designed inadvance), based on a given threshold.

As described above, this embodiment is characterized in detecting thedefect with respect to each group by grouping the whole regions of theinspection object to each pattern group constituting the region. Thisenables detection of the defect with a high accuracy even on a wafer notconstituted by regular pattern groups. Furthermore, it is also effectivefor the case in which the same inspection object is a multi-layer bondedwafer, especially with each layer having irregular pattern groups.

Reference numerals 141, 142, 143 in FIG. 14 schematically show arrays ofeach layer in a three-layer bonded wafer used as the inspection object.Each layer of the wafer is constituted by chips having a plurality ofdifferent pattern groups (indicated by different hatch patterns).

When viewing the chips on the wafer in the depth direction, thecombinations of the pattern groups (group A146 and group B147) aredifferent. Lines 144, 145 in FIG. 14 indicate the chips superimposed inthe depth direction. On the first layer of the wafer 141, the samepatterns are formed on a chip 1441 on the line 144 and a chip 1451 onthe line 145, while the patterns formed on a pattern 1442 on the line144 and a pattern 1452 on the line 145 on the second layer of the wafer142 are different and the patterns formed on a pattern 1443 on the line144 and a pattern 1453 on the line 145 on the third layer of the wafer143 are also different. In such a case, it is possible to use groupinginformation of any one of the wafers 141 to 143 depending on where thebonding surface to be inspect is located, and it is also possible togenerate combined grouping information of the these wafers to becommonly used for all the bonding surfaces.

The group A146 and the group B147 in FIG. 14 show the label informationof each chip when the chips on the lines 144 and 145 are superimposed inthe depth direction. The label information may be newly groupeddepending on the difference of the label combination designating theregion having the chip combination with the same pattern as the chippattern on the line 144 formed thereon as a label A and the regionhaving the chip combination with the same pattern as the chip pattern onthe line 145 as a label B, and it is stored as the label informationuniquely determined for the bonded wafer. The label information isautomatically set according to the combination pattern in the depthdirection based on the label information of each layer of the wafer.

According to this embodiment, even though the image obtained from theinspection object includes aperiodic patterns, the image is segmentedand grouped into regions having the same pattern group and a defectionis detected within the partial images belonging to the same group. Thus,even when the image obtained from the inspection object includes suchaperiodic patterns, it is possible to segment and group such an imageinto the regions having the same pattern group and detect the defectwithin the partial images belonging to the same group.

Second Embodiment

The implementation of the inspection method according to the presentinvention and the apparatus thereof is described above taking an exampleof a substrate having a multi-layer structure and a complicated patternsuch as a semiconductor wafer and a MEMS (Micro Electro Mechanical)wafer as the inspection object, and it is also applicable to aninspection of an IC package mounted on an IC tray or the like.

One example is shown in FIGS. 15A and 15B. Denoted by 150 in FIG. 15A isan IC tray, and labels A, B, C, and D in each pocket of the IC tray 150indicate different types and model numbers of the IC packages placed inthe IC tray. FIG. 15B shows a processing procedure according to thisembodiment. With the inspection method of the present invention and theapparatus thereof, tray matrix information 152 of the IC packageincluding the type and the model number (model number of IC packageplaced in each pocket on the tray, and the like) is received along withan inspection recipe 151, tray pockets are grouped based on the traymatrix information 152 (S1500), images of the pockets belonging to thesame group are collected from images 153 of the IC packages on the traypockets obtained (S1501), and the defect detection process describedwith reference to FIG. 8 in the first embodiment is performed at thedefect detection unit 7-2. The same process is performed on each group.This enables a highly sensitive inspection even when multiple types ofIC packages are placed on a single IC tray.

The above processing is also effective for the inspection of the ICpackage formed on a strip substrate. Instead of labeling each pocket onthe IC tray, labels may be applied according to the type of the deviceplaced therein or the pattern group of the obtained image, and the sameprocessing is applied thereafter.

Embodiments of the present invention are described above taking anexample of the defect inspection using the ultrasonic inspectionapparatus in a case where there are multiple types of devices formed ona wafer or an IC tray, but it is also effective for an inspection of adiscrete IC package. In this case, the reference image is generated froma good sample with respect to each type in advance; the correspondingreference image is input according to the type of the inspection object;and the defect accuracy is calculated to make a determination. Whenthere is only one type of constructions formed on the wafer or ICpackages placed on the IC tray and the obtained image of the inspectionobject is constituted by a regular pattern, the present inspectionmethod can be used by applying the same labels to all the regions.

The present invention is applicable not only to images obtained by theultrasonic inspection apparatus but also to non-destructive inspectionimages obtained by an x-ray defect inspection apparatus and images ofappearance inspection.

The present invention has been specifically described above based on itsembodiments, and it is obvious that the present invention is not limitedto the embodiments and various modifications can be made withoutdeparting from the spirit of the invention.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiment is therefore to be considered in all respects as illustrativeand not restrictive, the scope of the invention being indicated by theappended claims, rather than by the foregoing description, and allchanges which come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

The invention claimed is:
 1. A defect inspection method comprising thesteps of: (1) obtaining an image of an inspection object, for which aplurality of types of patterns are formed, using an ultrasonic wave; (2)dividing the image into a plurality of partial images; (3) grouping theplurality of partial images into a plurality of groups eachcorresponding to one said type of said plurality of types of patterns;(4) for a first group among said plurality of groups, generating a firstreference image that does not include a defect; (5) for said firstgroup, generating a first multi-value mask for masking a non-defectivepixel from partial images of said first group; (6) for said first group,calculating first defect accuracies by matching brightnesses of thepartial images of said first group and said first reference image; and(7) detecting defects based on the accuracies and the multi-value mask.2. An ultrasonic inspection apparatus comprising: an ultrasonic probeemitting an ultrasonic wave; a flaw detector detecting a reflected echogenerated from an inspection object by the ultrasonic wave emitted fromthe ultrasonic probe; an A/D convertor converting a signal output fromthe flaw detector; and an image processor configured to (1) obtain animage of the inspection object, to which a plurality type of patternsare formed, using the signal output via the A/D convertor; (2) extract aplurality of partial images from the obtained image; (3) group theplurality of partial images into a plurality of groups corresponding tothe types of pattern; (4) for a first group among the groups, generate afirst reference partial image; (5) for the first group, generate a firstmulti-value mask partial images of the first group; (6) for the firstgroup, calculate first defect accuracies based on brightness of thepartial images of the first group and the first reference partial image;and (7) detect defects based on the first defect accuracies and thefirst multi-value mask.
 3. The ultrasonic inspection apparatus accordingto claim 2, wherein the image processor is further configured to: (7A)for the detection of the defects in (7), detect defect positions on thepartial images of the first group, based on the first defect accuraciesand the multi-value mask; and (8) map the defect positions on thepartial images of the first group, to the obtained image.
 4. Theultrasonic inspection apparatus according to claim 2, wherein each ofthe partial images corresponds to each chip on the inspection object. 5.The ultrasonic inspection apparatus according to claim 2, wherein thegrouping of the plurality of partial images in (3) is performed based onlabels applied to segmented regions by a user.
 6. The ultrasonicinspection apparatus according to claim 2, wherein the grouping of theplurality of partial images in (3) is performed based on design data oran exposure recipe used for the inspection object.
 7. An ultrasonicinspection apparatus according to claim 2, wherein the generating of thefirst reference partial image in (4) is performed based on a result ofstatistical processing of the partial images of the first group, andwherein the image processor is further configured to: (4a) for a secondgroup among the groups, generate a second reference partial image basedon information on a good sample guaranteed to be free of defect; (5a)for the second group, generate a second multi-value mask from partialimages of the second group; (6a) for the second group, calculate seconddefect accuracies based on brightness of the partial images of thesecond group and the second reference partial image; and (7a) detectdefects based on the second defect accuracies and the second multi-valuemask.
 8. An ultrasonic inspection apparatus comprising: an ultrasonicprobe emitting an ultrasonic wave; a flaw detector detecting a reflectedecho generated from an inspection object by the ultrasonic wave emittedfrom the ultrasonic probe; an A/D convertor converting a signal outputfrom the flaw detector; and an image processor, wherein the inspectionobject is a multi-layer wafer comprising a plurality of wafer eachformed a plurality types of patterns, wherein the image processorconfigured to (1) obtain an image of the inspection object using thesignal output via the A/D convertor, the image is a sectional image ofthe inspection object; (2) extract a plurality of partial images fromthe obtained image; (3) group the plurality of partial images into aplurality of groups corresponding to the types of pattern, based on eachtype of pattern formed on a region corresponding to the partial imagesabout each wafer; (4) for a first group among the groups, generate afirst reference partial image; (5) for the first group, generate a firstmulti-value mask from partial images of the first group; (6) for thefirst group, calculate first defect accuracies based on brightness ofthe partial images of the first group and the first reference partialimage; and (7) detect defects based on the first defect accuracies andthe multi-value mask.
 9. The ultrasonic inspection apparatus accordingto claim 8, wherein the image processor is further configured to: (7A)for the detection of the defects in (7), detect defect positions on thepartial images of the first group, based on the first defect accuraciesand the multi-value mask; and (8) map the defect positions on thepartial images of the first group, to the obtained image.
 10. Theultrasonic inspection apparatus according to claim 8, wherein each ofthe partial images corresponds to each chip on the inspection object.11. The ultrasonic inspection apparatus according to claim 8, whereinthe grouping of the plurality of partial images in (3) is performedbased on labels applied to segmented regions by a user.
 12. Theultrasonic inspection apparatus according to claim 8, wherein thegrouping of the plurality of partial images in (3) is performed based ondesign data or an exposure recipe used for the inspection object.
 13. Anultrasonic inspection apparatus according to claim 8, wherein thegenerating of the first reference partial image in (4) is performedbased on a result of statistical processing of the partial images of thefirst group, and wherein the image processor is further configured to:(4a) for a second group among the groups, generate a second referencepartial image based on information on a good sample guaranteed to befree of defect; (5a) for the second group, generate a second multi-valuemask from partial images of the second group; (6a) for the second group,calculate second defect accuracies based on brightness of the partialimages of the second group and the second reference partial image; and(7a) detect defects based on the second defect accuracies and the secondmulti-value mask.